Triple
T25127518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1977 Bucharest earthquake |
E629433
|
entity |
| Predicate | buildingsCollapsedInBucharest |
P1583
|
FINISHED |
| Object | over 30 large buildings |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: over 30 large buildings | Statement: [1977 Bucharest earthquake, buildingsCollapsedInBucharest, over 30 large buildings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: buildingsCollapsedInBucharest Context triple: [1977 Bucharest earthquake, buildingsCollapsedInBucharest, over 30 large buildings]
-
A.
buildingsDestroyed
chosen
Indicates that one or more buildings have been damaged to the point of destruction as a result of some event or action.
-
B.
destroyedCity
Indicates that an entity has caused the complete or near-complete destruction of a city.
-
C.
cityRebuiltAfter
Indicates that a city was reconstructed or significantly restored following a prior event of destruction or severe damage.
-
D.
reconstructedBuilding
Indicates that a building has been rebuilt or restored after damage, destruction, or significant alteration.
-
E.
originalBuildingDestroyedBy
Indicates that the original building was destroyed as a result of the actions or effects of the specified agent or cause.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e2ff3288048190bd82c3b7f7bd0e62 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f465f4fd9c81909bd0c65aee986554 |
completed | May 1, 2026, 8:36 a.m. |
| PD | Predicate disambiguation | batch_69f45cfb53f4819099bba48c5057e787 |
completed | May 1, 2026, 7:57 a.m. |
Created at: April 18, 2026, 6:28 a.m.